NDLI: Locally Weighted Fusion of Multiple Predictive Models

Content Provider	IEEE Xplore Digital Library
Author	Feng Xue Subbu, R. Bonissone, P.
Copyright Year	2006
Abstract	Fusing the outputs from an ensemble of models in an effective way can often boost overall model accuracy. This paper presents a novel method, called locally weighted fusion, which aggregates the results of multiple predictive models based on local accuracy measures of these models in the neighborhood of the probe point for which we want to make a prediction. While we demonstrate the method in the context of multiple neural network models, the concepts may be applied to other predictive techniques as well. This fusion method is applied to develop highly accurate models for emissions, efficiency, and load prediction in a complex real-world power plant. The locally weighted fusion method boosts the predictive performance by 20-40% over the baseline single model approach for the various prediction targets. Relative to this approach, fusion strategies which apply averaging or globally weighting only produce a 2-6% performance boost over the baseline.
Starting Page	2137
Ending Page	2143
File Size	412133
Page Count	7
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.246985
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Neural networks Uncertainty Aggregates Probes Context modeling Power generation Power system modeling Engineering management Maintenance engineering multiple models Bootstrapping information fusion neural network ensemble
Content Type	Text
Resource Type	Article

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